Decision fusion is a widely used technique for several kinds of classification applications such as, for example, medical imaging, biometric verification, signature or fingerprint recognition, robot vision, speech recognition, image retrieval, expert systems etc.
Generally, in decision fusion applications, multiple classifiers (or experts) perform separate classification experiments on respective data sets, and consequently designate a nominated class as correct. The classifier decisions are then combined in a predetermined selection strategy to arrive at the final class, as described below. Two extreme approaches for the combination strategy are outlined below:    1. The first approach may accept the decision of the majority of the classifiers as the final decision (decision consensus approach).    2. The second approach can take the decision of the most competent expert as the final decision (most competent expert approach).
An intermediate approach involves determining a solution in which a consensus decision is evaluated in terms of the past track records of the experts. Instead of directly accepting the consensus decision, the reliability of each decision is evaluated through various kinds of confidence measures. The decision is either accepted or rejected based on the result of such an evaluation.
In a further approach, a Bayesian cost function is minimized over all the decisions given by the experts. The cost function is defined as the cost of making a wrong decision multiplied by the joint probability of occurrence of the respective decisions.
None of the above approaches outlined above are rigorously optimal or universally applicable, and can be subject to errors or limitations of one kind or another. Accordingly, it is an object of the invention to at least attempt to address these and other limitations associated with the prior art. In particular, it is an object of the invention to generally improve the classification accuracy of particular decision fusion applications which rely on one of the prior art approaches outlined above.